"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"hv.extension('plotly')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A ``BoxWhisker`` Element is a quick way of visually summarizing one or more groups of numerical data through their quartiles. \n",
"\n",
"The data of a ``BoxWhisker`` Element may have any number of key dimensions representing the grouping of the value dimension and a single value dimensions representing the distribution of values within each group. See the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Without any groups a BoxWhisker Element represents a single distribution of values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hv.BoxWhisker(np.random.randn(1000), vdims='Value')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By supplying key dimensions we can compare our distributions across multiple variables."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"groups = [chr(65+g) for g in np.random.randint(0, 3, 200)]\n",
"\n",
"box = hv.BoxWhisker((groups, np.random.randint(0, 5, 200), np.random.randn(200)),\n",
" ['Group', 'Category'], 'Value').sort()\n",
"\n",
"box.opts(height=400, width=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For full documentation and the available style and plot options, use ``hv.help(hv.BoxWhisker).``"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}